Electrocardiography is the recording of the electrical activity of the heart over time via skin electrodes. An electrocardiogram (ECG) is used by cardiologists to aid in the diagnosis of various cardiac abnormalities. Cardiac arrhythmia and ischemia are some of the conditions that are identified through the analysis of ECG. There is a strong correlation between the ST segment deviation and the incidence of ischemia and thus ST segment deviation measurement is an important parameter in clinical study. Further, morphology of the ST segment is an important clinical parameter in identifying a type of heart attack. Some of these types of heart attacks are ST Elevation Myocardial Infarction (STEMI) and Non ST Elevation Myocardial Infarction (NSTEMI) which can be identified through ST segment morphology. Further, the shape/geometry of the ST morphology can also be used as an indicator of an impending heart attack and also to understand the severity of the occurred heart attack.
However, automated measurement of ST segment deviation and classification of ST segment morphology presents technical difficulties like the presence of noise in the signal, baseline wander of the signal and so on.
Accordingly, there is a need for a robust technique of automated ST segment deviation computation.